PhD Student – Department of Mechanical and Industrial Engineering,
Demand is high for professionals who can analyze data to derive organizational insights and apply them to predict future outcomes. Yet it’s estimated that Canada faces a shortage of up to 19,000 professionals with data and analytical skills, and 150,000 with deep analytical literacy.
To fill this gap, leading academic institutions are developing programs to provide the next generation of graduates with technical and applied knowledge in these specialized areas. With opportunities in industry, government and research, graduates of these new programs can look forward to careers as data scientists, data analysts, business managers, chief data officers, data solutions architects, and business analysts.
Career flexibility and a chance to make an impact
Shirin Akbarinasaji is among this next generation of graduates. A third year doctoral student in data sciences with Ryerson University’s Faculty of Engineering and Architectural Science (FEAS), the vast career options appeal to her. “There is a necessity in every business—from finance to retail to health — to make intelligent, customer-centric decisions.”
Professional flexibility wasn’t the only reason Akbarinasaji chose to study data sciences. As a talented math student, she’s fascinated by Artificial intelligence (AI) and machine learning, and their useful applications in the real world. “By teaching machines to learn, reason and predict, I can help decision-makers reduce uncertainty in their day-to-day activities,” she says.
It’s a great opportunity to be part of an analytic architecture team. Everything I’m learning will help prepare me for my future career.Shirin Akbarinasaji
At Ryerson, Akbarinasaji is taking foundational courses in machine learning, programming and statistics, and receives invaluable mentorship from faculty. She also gets connected to industry research opportunities through Ryerson’s Data Science Laboratory, and is currently working with IBM to develop a predictive model for bug prioritization. “It’s a great opportunity to be part of an analytic architecture team,” she says. “Everything I’m learning will help prepare me for my future career.”
It’s a career she wouldn’t have been able to dream of as a child. “All the work being done in AI was strictly theoretical back then,” she says. “But now, thanks to the abundance of data, coupled with computational power, AI is very real.”
And the field keeps evolving. In the few years that she’s been studying AI, Akbarinasaji has seen it grow far beyond the borders of Silicon Valley. “From marketers who use it to study customer behaviour, to health professionals who apply it to create personalized treatments, every industry is now trying to benefit from the technology.”
Keeping up with the technology and speaking the language
Part of the satisfaction of working in AI comes from mastering its challenges. Keeping up with the fast pace of technology is a priority for Akbarinasaji, as is staying on top of all of the technical skills she requires, such as programming, Big Data platform design, data structure and algorithms, statistics, and machine learning.
Equally satisfying is staying on top of the ever-changing ‘language’ requirements. “For instance, if data comes from health care we need to learn the language of medicine,” she says. “If data comes from software, we need to learn the developer’s language.” It’s why Akbarinasaji stresses the importance of soft skills like communication, collaboration and teamwork to all future students of data science. “After all,” she says, “at the end of the day, you need to be able to communicate your findings to your client.”